This course takes an application-driven approach to current topics in machine learning. The course covers supervised learning, unsupervised learning, semi-supervised learning, and several other learning settings. We cover popular algorithms and focus on how statistical learning algorithms are applied to real world applications. Students implement several learning algorithms throughout the semester.

This course has three main goals:

1. Students will learn the fundamentals of machine learning

2. Students will learn to implement machine learning algorithms

3. Students will learn how to apply machine learning to different settings and how to evaluate the results and models they obtain

Course taught in Spring 2020 and previously taught alongside Dr. Jared Markowitz in Spring 2018 (course site).

I assisted in converting this to an online course, focusing on two lectures covering gravitational waves. In the first lecture, we derive classical gravitational waves using a retarded Newtonian approximation. In the second lecture, we derive gravitational waves in general relativity, using weak field and linear approximations. This allows us to obtain many useful results about that properties of gravitational waves and how they may be detected.

Course Description: After a brief review of the theory of special relativity, the mathematical tools of tensor calculus that are necessary for understanding the theory of general relativity will be developed. Relativistic perfect fluids and their stress-energy-momentum tensor will be defined, and Einstein's field equations will be studied. Gravitational collapse will be introduced, and the Schwarzschild Black Hole solution will be discussed.

Course taught in Spring 2021 and 2022 semesters.